Many enhanced oil recovery (EOR) processes such as chemical, miscible and steam flooding are associated with complex flow mechanisms that manifest at the displacement front. Viscous fingering, polymer dilution and thermal effects are some of these mechanisms. Accurate modeling requires simulations on high resolution grids to properly capture the physics in the vicinity of the displacement front. The grid resolution in some applications needs to be at scales much smaller than typical grid resolutions (for example, 10-30 ft. vs. 100 ft.) used in modeling primary and secondary recovery processes. However, fine grid resolutions incur longer simulation times. Thus, past efforts at running full-field chemical EOR and thermal simulations were frequently deemed impractical resulting in limited use of reservoir simulation as a reliable tool for EOR reservoir management.
Parallel computing is the most common approach used to run high resolution models. This approach, however, is not practical in workflows that require simulations on many models to better manage uncertainties. Furthermore, access to massively parallel machines, which are required for full field simulations, is usually limited. Dynamic gridding is another approach that has been used to solve this problem. This approach modifies the grid resolution as needed during run time. The existing dynamic gridding approaches have challenges, such as efficiently managing the computational overhead to modify the grid resolution at run time and adequately capturing the appropriate level of heterogeneity in the modified cells.
Dynamic gridding has been implemented in the past with limited success due to the intensive computational requirements and therefore, past efforts for running full-field chemical EOR and thermal simulations were frequently deemed impractical resulting in limited use of reservoir simulation as a reliable tool for EOR reservoir management. Embodiments of the disclosure include new, more efficient methods to implement dynamic gridding.